8 thoughts on “Week 2 – Vision & Colors, R-ggplot2”

I regularly use Esri ArcGIS and have found that choosing colors is one of the trickiest part to making a map. The Colorbrewer article drove the point home that default schemes can often times be unsatisfactory. The lecture also emphasized that colors come with a biological and psychological barriers. One example of colors that I recently used was for differentiating between census tracts that were predominantly male of female in Boston. I found that you could represent the genders by any color, but blue for male and pink is a scheme that is very recognizable for most people. I also used graduated colors for the varying percentages of gender density and found the point from class that colors are not necessarily the best representation for numerical values rang true here. As data viz designers, colors play a huge role in the narrative and we have to work with the ways in which we perceive the world around us.

I also agree that there are so-called “common sense” colors, like blue for men, pink for ladies. Do you go to Columbia gym? Do you notice the locker keys, tied with color strips. Guys are red strips, girls are blue strips. What do you think, they “mix” up the colors?
Would you like share your census tracts example here and tell us more about it?

Color Brewer is clearly a useful collection of palettes, being both easily readable and aesthetically pleasing. We’ll no doubt see more and more charts (and not just maps) making use of these color schemes, especially since they are included with ggplot, Hadley Wickham’s free and hugely popular R package (see http://www.google.com/trends/explore#q=ggplot%2C%20ggplot2&cmpt=q for a quick and dirty illustration of ggplot’s ascendancy).

For now, the “color brewer look” is an attractive alternative to some of the common defaults, like black & white or the built-in palettes in Microsoft Excel. But recall Andrew Gelman’s paper, which distinguished between the needs of academic statistical graphics and mass media infographics. The former favor familiar constructs, while the latter favor novel, eye-catching design. Accordingly, I wonder if the “Color Brewer look” will lose some of its cachet as it becomes more familiar and routine–at least for those users in the “mass media infographic” space.

An important issue with respect to visualizing data is the clarity of information, which the use of color should enhance rather than limit. An issue raised in the reading is the potential problem raised by using too many colors, so that legibility is compromised. In a sense, this defeats at least part of the purpose of providing such visual displays, which is that relative to tables of summary statistics and regression results, graphing requires that the researcher consider how best to cleanly and directly present his findings (see Kastellec & Leoni 2007, who find that when the goal of presentation is comparison, good statistical graphs consistently outperform tables). It is important, then, to maintain simplicity of presentation so that the graph form and colors are in fact illuminating rather than obscuring the key findings, a method of which is to avoid using only slightly different colors in a complex map, or presenting colors in a nicely ordered sequence.

I did quite a few plots before coming to this class, but I have never thought of the importance of the choice of colors. Of course, we want to choose nice colors for our plots, but it is my first time reading and thinking about the different way of using colors in different kinds of plots. For example, we might use colors to separate the numbers data of classes, use sequential scheme for population density, or use diverging scheme for population change with two hues. Here I found a very good tutorial about colors that some of you might be interested in: http://goo.gl/Tprxh.

The colorbrewer article was a nice conceptual introduction to the use of color, steeped in a practical set of guidelines. However, I wonder whether such an introduction is as necessary now that many of these rules and instructions are increasingly packaged within commercial and open source offerings. For instance, http://www.mapbox.com is a commercial DIY map design service that steers users towards using many of the design principals espoused in this article, but adds an additional layer of functionality to that through the use of open source tools.